Shuheng Zhou
I am Professor of Statistics at the University of California, Riverside. My research spans high dimensional statistics, machine learning, algorithms and privacy.
Teaching | Publications | Bio
email: szhou@ucr.edu
Embedded Files
News: Sep. 2026
I am incredibly honored and humbled to be elected as a Fellow of the Institute of Mathematical Statistics (Class of 2026).
A heartfelt thank you to my nominator and letter writers for their generous support, and to my advisors, postdoc mentors, collaborators, and students who continue to challenge, encourage, and inspire me every day.
Selected Papers
The authors on theory papers are alphabetically listed; Directly supervised students are underlined.
S. Zhou. Semidefinite programming relaxations and debiasing for MAXCUT-based clustering. Revision submitted to Journal of Machine Learning Research, July 2026. 64 pages. pdf
S. Zhou. Thresholded Lasso for high dimensional variable selection. Annals of the Institute of Statistical Mathematics, Published Online December, 2025. Preliminary abstract appeared in Advances in Neural Information Processing Systems 22. Preprint / Online Version
S. Zhou. Concentration of measure bounds for matrix-variate data with missing values. Bernoulli 30(1), pp 198–226, 2024. Originally posted on arXiv 2008.03244 titled "The tensor quadratic forms" (71 Pages), August 2020. pdf / Journal Link
S. Zhou and K. Greenewald. Sharper rates of convergence for the tensor graphical Lasso estimator. Proceedings of 2024 IEEE International Symposium on Information Theory (ISIT 2024), Pages 533-588. Athens, Greece. pdf / link
K. Greenewald, S. Zhou, and A. Hero. The tensor graphical Lasso (TeraLasso). Journal of Royal Statistical Society, Series B 81 (5), pp 901--931, 2019. pdf / Journal Link
M. Hornstein, R. Fan, K. Shedden, and S. Zhou. Joint mean and covariance estimation with unreplicated matrix-variate data. Journal of the American Statistical Association, Vol. 114 (526), pp 682--696, 2019. pdf / R package
S. Zhou. Sparse Hanson-Wright inequalities for subgaussian quadratic forms. Bernoulli, Vol. 25 (3), pp 1603--1639, 2019. pdf / Journal Link
M. Rudelson and S. Zhou. Errors-in-variables models with dependent measurements. Electronic Journal of Statistics, Vol. 11 (1), pp 1699--1797, 2017. pdf / Journal Link
K. Greenewald, S. Park, S. Zhou, and A. Giessing. Time-dependent spatially varying graphical models, with application to brain fMRI data analysis. Advances in Neural Information Processing Systems 30, 2017. pdf
T.H. Chan, A. Gupta, B.M. Maggs and S. Zhou. On hierarchical routing in doubling metrics. ACM Transactions on Algorithms (TALG) Vol. 12 (4), pp 1--22, 2016. pdf
S. Zhou. Gemini: Graph estimation with matrix variate normal instances. Annals of Statistics, Vol. 42 (2), pp 532--562, 2014. Journal Link
M. Rudelson and S. Zhou. Reconstruction from anisotropic random measurements. IEEE Transactions on Information Theory, Vol. 59. (6), pp 3434--3447, 2013. pdf / talk slides
T. Tsiligkaridis, A. Hero, and S. Zhou. On convergence of Kronecker graphical Lasso algorithms. IEEE Transactions on Signal Processing ,Vol. 61 (7), pp 1743–1755, 2013. pdf
A. Kalaitzis, J. Lafferty, N. D. Lawrence, and S. Zhou. The Bigraphical Lasso. Proceedings of the 30th International Conference on Machine Learning (ICML), PMLR 28(3):1229–1237, 2013. Link
S. Zhou, P. Rutimann, M. Xu, and P. Bühlmann. High-dimensional covariance estimation based on Gaussian graphical models. Journal of Machine Learning Research, Vol. 12 (91), pp 2975--3026, 2011. pdf / Journal Link
S. van de Geer, P. Bühlmann, and S. Zhou. The adaptive and the thresholded Lasso for potentially misspecified models (and a lower bound for the Lasso). Electronic Journal of Statistics 2011, Vol. 5, 688-749. Journal Link
L. Wasserman and S. Zhou. A Statistical framework for differential privacy. Journal of the American Statistical Association, Vol. 105 (489), pp 375--389, 2010 (Appeared as a featured article). pdf
S. Rao and S. Zhou. Edge disjoint paths in moderately connected graphs. SIAM Journal on Computing, Vol. 39 (5), pp 1856--1887, 2010. Journal version
S. Zhou, J. Lafferty, and L. Wasserman. Time varying undirected graphs. Machine Learning Journal, Vol 80, Numbers 2--3, pp 295--319, 2010 (Invited and peer reviewed; Special Issue on Learning Theory). pdf
S. Zhou, J. Lafferty, and L. Wasserman. Compressed and privacy sensitive sparse regression. IEEE Transactions on Information Theory, Vol. 55 (2), pp 846--866, 2009. Preliminary abstract appeared in Advances in Neural Information Processing Systems (NIPS) 20. pdf
A. Blum, A. Coja-Oghlan, A. Frieze and S. Zhou. Separating populations with wide data: a spectral analysis. Electronic Journal of Statistics, Vol. 3, pp 76--113, 2009. Journal Link
S. Zhou, K. Ligett, L. Wasserman. Differential privacy with compression. Proceedings of 2009 IEEE International Symposium on Information Theory, Seoul, Korea, 2009. Preprint arXiv:0901.1365 / Link
Software Package
Directly supervised students are underlined.
Michael Hornstein, Roger Fan, Kerby Shedden and Shuheng Zhou. “jointMeanCov: Joint Mean and Covariance Estimation for Matrix-Variate Data.” The Comprehensive R Archive Network. http://cran.r-project.org. Available since 2019. This package contains algorithms and functions for jointly estimating two-group means and covariances for matrix-variate data and calculating test statistics.
Selected Talks
Selected Talks
Semidefinite programming relaxations and debiasing for MAXCUT-based clustering. Joint Statistical Meetings (JSM), Boston, MA, USA, August 2026.
Concentration of measure bounds for matrix-variate data with missing values. Seminar for Statistics, Department of Mathematics, ETH Zürich, Switzerland, December 2023.
Tensor graphical models for complex and high dimensional data. Algorithmic Advances for Statistical Inference with Combinatorial Structure, Simons Institute for the Theory of Computing, Berkeley, CA, Oct. 11-15, 2021. Abstract, Video
Reconstruction from anisotropic random measurements. Coding, Complexity, and Sparsity Workshop (SPARC), University of Michigan, Ann Arbor, MI, August 5-7, 2013. slides
High-dimensional covariance estimation based on Gaussian graphical models. IMA workshop on High Dimensional Phenomena, University of Minnesota, Minneapolis, MN, September 2011. slides
Thresholded Lasso for high dimensional variable selection. Probability and Geometry in High Dimensions, Université Paris-Est Marne-la-Vallée, May 2010. slides
Google Sites
Report abuse